Research on Personalized Motion Difficulty Dynamic Adjustment Algorithm for VR Rehabilitation Sports Based on Reinforcement Learning

Virtual Reality (VR) rehabilitation has rapidly gained recognition as a transformative and effective tool in modern therapeutic practices, offering immersive, interactive environments that significantly enhance patient participation, motivation, and recovery outcomes. Traditional rehabilitation methods, although clinically validated, often suffer from limited adaptability, relying on static difficulty levels that fail to reflect the complex, evolving needs of individual patients. This rigidity can lead to reduced engagement, plateaued progress, and diminished therapeutic efficacy. To overcome these limitations, this study introduces a novel, intelligent algorithm designed to dynamically calibrate the difficulty of VR-based rehabilitation exercises in real time. By employing advanced reinforcement learning techniques, the algorithm continuously monitors patient movements, reaction times, and task performance to adjust exercise parameters on the fly. This adaptive mechanism maintains an optimal level of challenge, ensuring that therapy remains personalized, engaging, and effective across different stages of recovery. Experimental evaluations demonstrate that this approach significantly improves both short-term and long-term rehabilitation outcomes compared to traditional static frameworks. The findings suggest a promising future for adaptive VR therapy systems in clinical settings, with broader implications for personalized medicine. This research aligns closely with the scope of Frontiers in Computer Science, particularly within the realm of human-media interaction and intelligent health technologies designed to address real-world therapeutic challenges.